code: def test_predict_feature_columns_pandas_categorical(to_string: bool):
      pandas_data = pd.DataFrame(dummy_data, columns=["A", "B"])
      if to_string:
          pandas_data["A"] = [str(x) for x in pandas_data["A"]]
      pandas_data["A"] = pandas_data["A"].astype("category")
      pandas_target = pd.Series(dummy_target)
      pandas_model = (
          lgbm.LGBMClassifier(n_estimators=10)
          .fit(pandas_data, pandas_target)
          .booster_
      )
      preprocessor = DummyPreprocessor()
      predictor = LightGBMPredictor(
          model=pandas_model, preprocessor=preprocessor
      )
      data_batch = pd.DataFrame(
          np.array([[1, 2, 2], [3, 4, 8], [5, 6, 9]]),
          columns=["A", "B", "C"],
      )
      if to_string:
          data_batch["A"] = [str(x) for x in data_batch["A"]]
      data_batch["A"] = data_batch["A"].astype("category")
      predictions = predictor.predict(
          data_batch, feature_columns=["A", "B"]
      )
  
      assert len(predictions) == 3
      assert predictor.get_preprocessor().has_preprocessed
  
file_path: ray/python/ray/train/tests/test_lightgbm_predictor.py
imports: import re
  import lightgbm
  import numpy
  import pandas
  import pytest
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_pandas_to_batch_type
  from ray.train.lightgbm import LightGBMCheckpoint, LightGBMPredictor
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
label_desc: Use image node_id 7 for calling a global function with example usage: test_predict_feature_columns_pandas_categorical(to_string) without return types